Developing Shared Context Within Group Stories

2005 ◽  
pp. 232-247 ◽  
Author(s):  
Flávia Maria Santoro ◽  
Patrick Brézillon
Keyword(s):  
Shared Context

Towards a Global History of Culture1

2019 ◽  
Vol 51 (1) ◽  
pp. 135-146
Author(s):  
Yūji Nawata
Keyword(s):  
Global History ◽  
Short History ◽  
Physical Time ◽  
Shared Space ◽  
Cosmic Space ◽  
Parallel Universes ◽  
History Of ◽  
Shared Context

Abstract Contemporary physics often speaks of “multiverses” or “parallel universes,” seriously debating whether our cosmic space is only one of many2. However many such spaces there may be, for now let us limit ourselves to the space in which we find ourselves; let us focus furthermore on the planet we are on, and further still on humanity upon this planet. Let us attempt to write a short history of the culture produced by humanity on this globe. We humans possessed and indeed possess a shared space, the globe, where a physical time common to us all passes. Let us survey the history of the world’s culture within this shared context.

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

scholarly journals Show or Tell? Teaching with language outperforms demonstration but only when context is shared

2021 ◽  
Author(s):  
Theodore Sumers ◽  
Mark K Ho ◽  
Robert Hawkins ◽  
Tom Griffiths
Keyword(s):  
Real Time ◽  
Comparative Approach ◽  
High Fidelity ◽  
Linguistic Communication ◽  
Communicative Acts ◽  
Wide Range ◽  
The World ◽  
High Bandwidth ◽  
Abstract Language ◽  
Shared Context

People use a wide range of communicative acts, from concrete demonstrations to abstract language. What are the strengths and weaknesses of such different modalities? We present a series of real-time, multi-player experiments asking participants to teach (Boolean) concepts using either demonstrations or language. Our first experiment (N = 454) manipulated the complexity of the concept, finding that linguistic (but not demonstrative) teaching enables high-fidelity transmission of more complex concepts. Why, then, do humans use both demonstrations and language? As a form of conventionalized communication, language relies on shared context between speaker and listener, whereas demonstrations are inherently grounded in the world. We hypothesized linguistic communication would be more sensitive to perturbations of shared context than demonstrations. Our second experiment (N = 568) manipulated teachers’ ability to see the features that defined the concept. This restriction severely impaired linguistic (but not demonstrative) teaching. Our comparative approach confirms language relies on shared context to permit high bandwidth communication; in contrast, demonstrations are lower-bandwidth but more robust.

Eye-Gaze and Facial Expressions as Feedback Signals in Educational Interactions

2013 ◽  
pp. 38-58 ◽  
Author(s):  
Kristiina Jokinen ◽  
Päivi Majaranta
Keyword(s):  
Language Learning ◽  
Facial Expressions ◽  
Eye Gaze ◽  
Interactive Systems ◽  
Educational Systems ◽  
Future Prospects ◽  
Gaze Tracking ◽  
Educational Applications ◽  
Shared Context ◽  
Tracking Technology

In this chapter, the authors explore possibilities to use novel face and gaze tracking technology in educational applications, especially in interactive teaching agents for second language learning. They focus on non-verbal feedback that provides information about how well the speaker has understood the presented information, and how well the interaction is progressing. Such feedback is important in interactive applications in general, and in educational systems, it is effectively used to construct a shared context in which learning can take place: the teacher can use feedback signals to tailor the presentation appropriate for the student. This chapter surveys previous work, relevant technology, and future prospects for such multimodal interactive systems. It also sketches future educational systems which encourage the students to learn foreign languages in a natural and inclusive manner, via participating in interaction using natural communication strategies.

Value of Adaptation of Methodologies Between Different Knowledge Areas

2018 ◽  
pp. 221-248
Author(s):  
Eduardo Manchado-Pérez ◽  
Ignacio López-Forniés ◽  
Luis Berges-Muro
Keyword(s):  
Product Development ◽  
Learning Outcomes ◽  
Case Studies ◽  
Graphic Design ◽  
Industrial Design ◽  
Project Based Learning ◽  
Engineering Degree ◽  
First Moment ◽  
University Degree ◽  
Shared Context

Project-based learning (PBL) is a powerful tool for teaching that helps students to get the best in terms of ratio effort/learning outcomes, especially in studies with a very practical basis, such as university degree studies in engineering. A way of getting even more out of this is by means of the adaptation of methodologies from different knowledge areas, because this allows the launch of innovative ways of working with certain guarantees of success from the very first moment, and at the same time to integrate skills from different fields within a shared context. Furthermore, it helps to put into practice some transversal competences, which are very useful for future professionals. The chapter also includes some case studies on the successful adaptation of different methodologies coming from different fields such as graphic design, biology, and social sciences in the context of a university engineering degree in industrial design and product development.

Identifying Tribes on Twitter Through Shared Context

2019 ◽  
pp. 91-111 ◽  
Author(s):  
Peter A. Gloor ◽  
Andrea Fronzetti Colladon ◽  
Joao Marcos de Oliveira ◽  
Paola Rovelli ◽  
Manuel Galbier ◽  
...  
Keyword(s):  
Shared Context
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